| "Assisted reproductive technology" is known as one of the major scientific and technological invention that changed people's lives in the 20th century. As infertility patients, its emergence has brought the gospel, and has become the best way in infertility treatment. However, with the gradual development of science and technology, more and more infertility people choice assisted reproductive technology as treatment, whether it is more prone to birth defects or other pregnancy complications in their offspring born has aroused people's concern, and the safety problem of assisted reproductive technology has become the world's researchers hotly debated issue. Therefore, the understanding of assisted reproductive technology in neonatal outcomes particular in, whether to increase the risk of birth defects, and affecting factors has become common concern in the international community research area.Currently it has been mainly used Logistic regression methods to analyze the birth defects, but there are some limitations when it is analysis the influencing factors of low incidence. Therefore, the researchers combined the characteristics of Poisson distribution and developed a new regression model-Poisson regression model. It is a expansion of multi-variable nonlinear regression analysis, and its essence is a log-linear equations, and its theoretical foundation is based on Poisson distribution. On the one hand, it can be used to analyze a few factors affecting in the unit of time, space, space, or an occurred event. On the other hand, it also can be used in population-based rare diseases, health event data rates. Therefore, this study, we will use Poisson regression model to introduce the birth defects and influencing factors in assisted reproduction and natural conception, analyze the impact of various factors on the direct effects and association of birth defects, and provide a scientific basis for the prevention of newborn defects.ObjectiveThe main purpose of this study are: through Poisson regression model to explore the optimization of parameters ,and then compare the effects of different model parameters, set the appropriate parameters of the establishment of Poisson regression models in assisted reproduction and natural conception of newborn birth defects. Using model parameters to decide which model is fitted. This study use Poisson regression model to provide a reference for research, and confirm the factors affecting birth defects in newborns, explore the role of the relationship between the factors, and provide a scientific basis for birth defects prevention and intervention.Data and MethodsThe data collected from the project of "assisted reproductive technology micro-operational safety study" in Women's Hospital School of Medicine Zhejiang University. The total number of cases of assisted reproductive fertility is 1067 cases; the other natural conception is 2134 cases from2003 to 2007. The full of application of assisted reproductive technology pregnancies are as exposed group, and the natural conception in same period pregnancies are as un-exposed group, they are all the survey objects. Age is controlled conditions as confounding factors, stratified and use SPSS statistical software to random sample of 1:2 in the same conceived year as a non-exposed group, both of them establish the retrospective cohort. The study outcome is the structure birth defects in newborns. Neonatal diagnosis of structural malformations is according to International Classification of Diseases (ICD-10).Data analysis is mainly used SAS9.1 and Stata10.0 statistical software. The main analysis is included in descriptive statistical, single-factor regression and multi-factor analysis. The statistical methods are used for the Poisson regression and 1:2 conditions Logistic regression. Multivariate analysis is based on descriptive analysis and single-factor regression analysis, then systematically analysis and study of the factors affecting birth defects, and thus evaluation of birth defects in the superiority of the Poisson regression model for the study, while provide a reference for building a birth defects model.Results1. Poisson regression analysisAfter multivariate Poisson regression analysis, the results are as follows: model a said that: it revealed that relating to the birth defects influence factors are past history of abortion, neonatal sex and exposure factors (assisted reproductive technology). Compared with non-abortion in the past, the mother who has a history of birth abortion, the risk of birth defects in newborn has increased by about 1.833 times. Compared with newborn girls, boys have increased by about 1.650 times. Compared with natural conception, assisted reproductive conception has increased by about 1.962 times. The test of model goodness (Pearson x~2 =26.000, v =26, P > 0.05,) shows that it is a good model. Model b said that: the number of neonatal and maternal age is related to birth defects. Single baby is the reference, it is the more baby, the more risk of birth defects, by about 1.850 times. Compared with the mother's age 25-30 years old, when the age is too small, its birth defects significantly increased risk has increased by about 3 times. Model goodness of fit test (Pearson x~2 =21.293, v= 20, P> 0.05,) shows that it is a good model.2. Comparison the three methods of Poisson regressionComparing the three kinds of regression model, you can conclude that using forward method and backward method of Poisson regression to analysis the birth defects, the results are similar, there is no difference in screening independent variables between the two kinds of methods. But compared with the stepwise regression, there are more differences, mainly include the following aspects:First of all, from the results of the analysis point of view, the results of the former two models which analysis the major factor in birth defects are the past history of abortion and assisted reproductive fertility. The latter model analysis is the past history of abortion, neonatal sex and assisted reproductive fertility. Based on the literature, the latter results of the analysis are more reliable.Secondly, although the three models are a good fit, P> 0.05, according to the model since the variable selection criteria, "the greater the coefficient of determination, the better model fit", this principle can be drawn that, the coefficient in stepwise regression is slightly higher than the other two methods. Therefore, application of stepwise regression models to analysis the birth defects is a relatively good method.Finally, from three kinds of standard error of the regression model view, three kinds of regression models very similar, and show that the error in interpretation each variable on birth defects are similar.In summary, in birth defect data, the application of stepwise Poisson regression analysis is a better choice.In addition, for continuous variables, through the model goodness of fit test evaluation concludes that the model is better when continuous variables transferred into categorical variables, and then the coefficient is slightly higher. 3.1:2 condition Logistic regression analysisAfter 1:2 condition Logistic regression analysis, the results are as follows: model a said that: it is post that relating to the birth defects influence factors are past history of abortion and exposure factors (assisted reproductive technology). Compared with non-abortion in the past, the mother who has a history of birth abortion, the risk of birth defects in newborn has increased by about 2 times. Compared with natural conception, assisted reproductive conception has increased by about 2 times. The test of model goodness (Pearson x~2=20.908, v = 5, P <0.05,) shows that it is not a good model enough. Model b said that: the number of neonatal is related to birth defects. The more baby, the more risk of birth defects, it is by about 2 times risk. The test of model goodness (Pearson x~2 = 8.886, v=2, P <0.05,) shows that also it isn't a good model enough.4. Poisson regression compared with 1:2 conditions Logistic RegressionFirst of all, under the same conditions as independent variables, implicate respectively Poisson regression and 1:2 conditions, Logistic regression, we can see that, the former two models P >0.05, indicating that the model are fitting well. The latter two P <0.05, indicating that they are not good enough.Secondly, the former independent variables standard error is smaller than the latter corresponding, indicating the former is less errors independent variables.Finally, birth defects of the major factors, the former model results for the past history of abortion, neonatal gender, exposure factors, the number of newborns and mothers of child-bearing age. The latter model is only the results of past history of abortion, neonatal gender, exposure factors and neonatal number. Since the latter match the mother's birth age, and therefore its impact on birth defects are not well analyzed, the result is not accurate enough. Based on the above analysis, birth defects model fitting is best for the "Poisson regression model for birth defects in the multi-factor analysis." Conclusions1. By single factor and multivariate Poisson regression analysis, the main influence factors of birth defects are: the number of newborn, maternal age, past history of abortion, exposure factors. At the same time it also reveals the effect value of birth defects. In the final, the model is fit well.2. A comparative analysis of multivariate Poisson regression of the three methods of screening variables (stepwise, forward and backward method), we can conclude that stepwise method is relatively good in analysis the birth defects.3. A comparative of multivariate Poisson regression analysis and 1:2 conditions Logistic regression, it can be summed up that using Poisson regression model is a relatively perfect statistical method for the lower incidence of disease.4. Poisson regression will be a combination of mathematic and expertise knowledge, it is a satisfactory ways to deal with research data, and solved more methodological problems. Although with many unique advantages, it also has some limitations and shortcomings. |
